Zhe-Cheng Fan
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View article: Resource-Efficient Decoding of Topological Color Codes via Neural-Guided Union-Find Optimization
Resource-Efficient Decoding of Topological Color Codes via Neural-Guided Union-Find Optimization Open
Quantum error correction (QEC) is crucial for achieving reliable quantum computation. Among topological QEC codes, color codes can correct bit-flip and phase-flip errors simultaneously, enabling efficient resource utilization. However, exi…
View article: Quantum generative adversarial network with automated noise suppression mechanism based on WGAN-GP
Quantum generative adversarial network with automated noise suppression mechanism based on WGAN-GP Open
View article: Transformer-based quantum error decoding enhanced by QGANs: towards scalable surface code correction algorithms
Transformer-based quantum error decoding enhanced by QGANs: towards scalable surface code correction algorithms Open
View article: Transformer-Based Quantum Error Decoding Enhanced by QGANs: Towards Scalable Surface Code Correction Algorithms
Transformer-Based Quantum Error Decoding Enhanced by QGANs: Towards Scalable Surface Code Correction Algorithms Open
To address qubits' high environmental sensitivity and reduce the significant error rates in current quantum devices, quantum error correction stands as one of the most dependable approaches. The topological surface code, renowned for its u…
View article: Addressing the confounds of accompaniments in singer identification
Addressing the confounds of accompaniments in singer identification Open
Identifying singers is an important task with many applications. However, the task remains challenging due to many issues. One major issue is related to the confounding factors from the background instrumental music that is mixed with the …
View article: Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products
Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products Open
Vector-valued neural learning has emerged as a promising direction in deep learning recently. Traditionally, training data for neural networks (NNs) are formulated as a vector of scalars; however, its performance may not be optimal since a…
View article: SVSGAN: Singing Voice Separation via Generative Adversarial Network
SVSGAN: Singing Voice Separation via Generative Adversarial Network Open
Separating two sources from an audio mixture is an important task with many applications. It is a challenging problem since only one signal channel is available for analysis. In this paper, we propose a novel framework for singing voice se…
View article: Music Signal Processing Using Vector Product Neural Networks
Music Signal Processing Using Vector Product Neural Networks Open
We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-di…